For years, personalization has been retail’s magic word. The promise was simple: show each shopper exactly what they want, and they’ll buy more, stay longer and come back often.
Retailers bought into that promise — investing in segmentation models, recommendation engines and optimization rules. The idea was that if we could just collect enough data, we could predict exactly what a shopper wanted before they even asked.
But something strange happened.
Despite all the investment, most “personalized” experiences still feel oddly impersonal. Shoppers get shown items they’ve already purchased. Search results miss the mark. Recommendations use stale data from historical sessions and don’t consider what the shopper wants NOW.
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If we’re being honest, what’s widely labeled as personalization today is mostly guesswork.
The Moment of Realization
It starts with a simple scenario: A shopper searches for “boots.”
A traditional system pulls from past behavior, product co-purchases and rule-based merchandising boosts. It might prioritize bestsellers or winter styles based on the time of year.
But what if the shopper just moved to a colder climate? What if they’ve been browsing workwear lately? What if a snowstorm is on the way? None of those contextual clues are captured by static rules or legacy personalization logic.
This is where the current approach starts to crack. Because retail isn’t static — it’s dynamic, situational and often emotional. What matters most to shoppers is not just what they liked before, but what they need now.
That’s when the shift becomes clear: Personalization guesses. Context understands.
The Problem with Rules
Behind the scenes, many personalization engines run on rule-based logic:
- If a user views Product A → Recommend Product B
- If a user belongs to Segment X → Boost Category Y
- If a user searches “jeans” → Show top sellers
It’s all very tidy—until reality intervenes.
Retailers pile on new promotions, seasonal trends, business goals and customer exceptions. The logic becomes bloated, fragile and hard to manage. Each change risks breaking something else. Teams spend more time troubleshooting than innovating.
And even then, the system is slow to react. It can’t respond to what’s happening right now — like low inventory, a sudden price drop or a shopper switching devices mid-session.
Meanwhile, new users, new products and unusual searches often return irrelevant or empty results. No matter how large the data lake, rule-based systems don’t swim well in the long tail.
A Shift Toward Context
The breakthrough isn’t more rules. It’s a new way of thinking: contextual commerce.
Context means understanding a shopper’s intent in the moment — not based on who they were last week, but what they’re doing right now. It’s about capturing the why behind the behavior, not just the what.
This approach pulls from real-time session signals, semantic understanding of language, product relationships and business constraints — all blended together to determine the most relevant response in the moment.
It doesn’t matter whether the shopper typed “boots” or “cold weather shoes.” A context-aware system can interpret the intent, link it to behavior, inventory and external signals like weather — and respond with something truly relevant.
Why AI Changes the Game
Context is messy. It’s real-time, ambiguous and can’t be captured by static logic. And while the last 25 years trained us to search in a small handful of words, the past two years have us using up to 35 words per search. We’re now searching with a full question that also includes context.
This is where modern AI — especially transformer-based language models — becomes essential.
These models don’t just memorize product relationships or match keywords. They encode meaning. They reason over sparse or ambiguous input. They can take a vague phrase and derive actionable understanding.
And importantly, they don’t just react to what a shopper says — they infer what the shopper means. That difference unlocks a more fluid, responsive shopping experience.
What This Means for Retail
The shift from personalization to context isn’t just technical — it’s strategic.
Retailers that embrace contextual understanding will move faster, waste less and connect with shoppers more deeply. They’ll stop manually encoding business priorities into brittle systems and start influencing outcomes through high-level goals that AI can optimize dynamically.
Search results will become smarter — not just accurate, but intent-aware. Recommendations will adjust to the moment, not just the persona. And teams will spend less time maintaining rules and more time curating strategy.
Here’s the bottom line: We’re at the tipping point.
The personalization era showed what was possible with data. But it also exposed its limits. It relied on history when it should have focused on the present.
Now, the future of commerce lies in context. Context adapts. Context listens. Context understands.
And in a world where shopper expectations evolve by the second, that may be the most powerful retail capability of all.
John Andrews is Co-founder and CEO of Cimulate, an AI-native commerce platform that uses large language models to power context-aware search, recommendations and conversational shopping. A seasoned tech executive, he was previously the CEO of Celect, a retail analytics company acquired by Nike, where he became VP of Product Management in Demand & Supply Management. Earlier, he led product and marketing at Endeca, continuing in product strategy at Oracle Commerce post-acquisition. Andrews began his career at Deloitte Consulting and holds a B.A. from Boston College and an M.B.A. from Harvard Business School. He brings deep commerce expertise and a passion for innovation.
